Identifying Health Status in Grazing Dairy Cows from Milk Mid-Infrared Spectroscopy by Using Machine Learning Methods.
Ontology highlight
ABSTRACT:
SUBMITTER: Contla Hernandez B
PROVIDER: S-EPMC8388516 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:The goals of the current study were to investigate the effects of including chicory (Cichorium intybus L.) into the traditional feeding regime of ryegrass/white clover (Lolium perenne L./Trifolium repens L.), and time of its allocation on milk production, rumen fermentation, and FA composition of milk and rumen digesta of dairy cows. Nine groups of four cows were allocated one of three replicated feeding regimes: (1) ryegrass/white clover only (RGWC), (2) ryegrass/white clover + morning allocation of chicory (CHAM), and (3) ryegrass/white clover + afternoon allocation of chicory (CHPM). One cow per group had a rumen cannulae fitted. Treatment did not affect total grazing time or estimated dry matter intake, but cows ruminated more when fed RGWC than chicory. Allocating chicory in the afternoon elevated milk production compared with RGWC and CHAM. Milk from cows grazing chicory contained greater concentrations of polyunsaturated FA (PUFA) such as C18:3 c9, 12, 15 and C18:2 c9, 12 than those on RGWC. As with milk, rumen digesta concentration of PUFA increased when cows grazed on chicory rather than RGWC, which corresponded with lower concentrations of intermediate vaccenic and biohydrogenation end-product stearic acid for cows grazing on chicory. Mean ruminal pH was lower for cows offered chicory than those on RGWC, reflecting greater rumen concentrations of volatile fatty acids (VFA) for cows fed chicory. Allocating chicory during the afternoon is a useful strategy that can translate to improved milk production. The lower rumen pH, lower concentration of vaccenic and stearic acids, and elevated concentration of PUFA in the rumen of cows fed chicory suggest reduced biohydrogenation and may explain the elevated concentration of PUFA in the milk of cows fed chicory compared with those fed RGWC.
| S-EPMC7023442 | biostudies-literature
Project description:Genetic improvement of milk fatty acid content traits in dairy cattle is of great significance. However, chromatography-based methods to measure milk fatty acid content have several disadvantages. Thus, quick and accurate predictions of various milk fatty acid contents based on the mid-infrared spectrum (MIRS) from dairy herd improvement (DHI) data are essential and meaningful to expand the amount of phenotypic data available. In this study, 24 kinds of milk fatty acid concentrations were measured from the milk samples of 336 Holstein cows in Shandong Province, China, using the gas chromatography (GC) technique, which simultaneously produced MIRS values for the prediction of fatty acids. After quantification by the GC technique, milk fatty acid contents expressed as g/100 g of milk (milk-basis) and g/100 g of fat (fat-basis) were processed by five spectral pre-processing algorithms: first-order derivative (DER1), second-order derivative (DER2), multiple scattering correction (MSC), standard normal transform (SNV), and Savitzky-Golsy convolution smoothing (SG), and four regression models: random forest regression (RFR), partial least square regression (PLSR), least absolute shrinkage and selection operator regression (LassoR), and ridge regression (RidgeR). Two ranges of wavebands (4000~400 cm-1 and 3017~2823 cm-1/1805~1734 cm-1) were also used in the above analysis. The prediction accuracy was evaluated using a 10-fold cross validation procedure, with the ratio of the training set and the test set as 3:1, where the determination coefficient (R2) and residual predictive deviation (RPD) were used for evaluations. The results showed that 17 out of 31 milk fatty acids were accurately predicted using MIRS, with RPD values higher than 2 and R2 values higher than 0.75. In addition, 16 out of 31 fatty acids were accurately predicted by RFR, indicating that the ensemble learning model potentially resulted in a higher prediction accuracy. Meanwhile, DER1, DER2 and SG pre-processing algorithms led to high prediction accuracy for most fatty acids. In summary, these results imply that the application of MIRS to predict the fatty acid contents of milk is feasible.
| S-EPMC9864415 | biostudies-literature
Project description:Consumers are increasingly prone to request information about the production systems of the food they buy. For this purpose, certification and authentication methodologies are necessary not only to protect the choices of consumers, but also to protect producers and production systems. The objective of this preliminary work was to authenticate the grazing system of dairy goats using Near-Infrared Spectroscopy (NIRS) analyses of milk and faeces of the animals. Spectral information and several mathematical pre-treatments were used for the development of six discriminant models based on different algorithms for milk and faeces samples. Results showed that the NIRS spectra of both types of samples had some differences when the two feeding regimes were compared. Therefore, good discrimination rates were obtained with both strategies (faeces and milk samples), with classification percentages of up to 100% effectiveness. Discrimination of feeding regime and grazing authentication based on NIRS analysis of milk samples and an alternative sample such as faeces is considered as a potential approach for dairy goats and small ruminant production.
| S-EPMC10417735 | biostudies-literature
Project description:Background:Transformation of feed energy ingested by ruminants into milk is accompanied by energy losses via fecal and urine excretions, fermentation gases and heat. Heat production may differ among dairy cows despite comparable milk yield and body weight. Therefore, heat production can be considered an indicator of metabolic efficiency and directly measured in respiration chambers. The latter is an accurate but time-consuming technique. In contrast, milk Fourier transform mid-infrared (FTIR) spectroscopy is an inexpensive high-throughput method and used to estimate different physiological traits in cows. Thus, this study aimed to develop a heat production prediction model using heat production measurements in respiration chambers, milk FTIR spectra and milk yield measurements from dairy cows. Methods:Heat production was computed based on the animal's consumed oxygen, and produced carbon dioxide and methane in respiration chambers. Heat production data included 168 24-h-observations from 64 German Holstein and 20 dual-purpose Simmental cows. Animals were milked twice daily at 07:00 and 16:30?h in the respiration chambers. Milk yield was determined to predict heat production using a linear regression. Milk samples were collected from each milking and FTIR spectra were obtained with MilkoScan FT 6000. The average or milk yield-weighted average of the absorption spectra from the morning and afternoon milking were calculated to obtain a computed spectrum. A total of 288 wavenumbers per spectrum and the corresponding milk yield were used to develop the heat production model using partial least squares (PLS) regression. Results:Measured heat production of studied animals ranged between 712 and 1470?kJ/kg BW0.75. The coefficient of determination for the linear regression between milk yield and heat production was 0.46, whereas it was 0.23 for the FTIR spectra-based PLS model. The PLS prediction model using weighted average spectra and milk yield resulted in a cross-validation variance of 57% and a root mean square error of prediction of 86.5?kJ/kg BW0.75. The ratio of performance to deviation (RPD) was 1.56. Conclusion:The PLS model using weighted average FTIR spectra and milk yield has higher potential to predict heat production of dairy cows than models applying FTIR spectra or milk yield only.
| S-EPMC7204237 | biostudies-literature
Project description:Infrared (IR) plasmonic nanoantennas (PNAs) are powerful tools to identify molecules by the IR fingerprint absorption from plasmon-molecules interaction. However, the sensitivity and bandwidth of PNAs are limited by the small overlap between molecules and sensing hotspots and the sharp plasmonic resonance peaks. In addition to intuitive methods like enhancement of electric field of PNAs and enrichment of molecules on PNAs surfaces, we propose a loss engineering method to optimize damping rate by reducing radiative loss using hook nanoantennas (HNAs). Furthermore, with the spectral multiplexing of the HNAs from gradient dimension, the wavelength-multiplexed HNAs (WMHNAs) serve as ultrasensitive vibrational probes in a continuous ultra-broadband region (wavelengths from 6 μm to 9 μm). Leveraging the multi-dimensional features captured by WMHNA, we develop a machine learning method to extract complementary physical and chemical information from molecules. The proof-of-concept demonstration of molecular recognition from mixed alcohols (methanol, ethanol, and isopropanol) shows 100% identification accuracy from the microfluidic integrated WMHNAs. Our work brings another degree of freedom to optimize PNAs towards small-volume, real-time, label-free molecular recognition from various species in low concentrations for chemical and biological diagnostics.
| S-EPMC9256719 | biostudies-literature
Project description:BackgroundNitrate leaching to groundwater and surface water and ammonia volatilization from dairy farms have negative impacts on the environment. Meanwhile, the increasing demand for dairy products will result in more pollution if N losses are not controlled. Therefore, a more efficient, and environmentally friendly production system is needed, in which nitrogen use efficiency (NUE) of dairy cows plays a key role. To genetically improve NUE, extensively recorded and cost-effective proxies are essential, which can be obtained by including mid-infrared (MIR) spectra of milk in prediction models for NUE. This study aimed to develop and validate the best prediction model of NUE, nitrogen loss (NL) and dry matter intake (DMI) for individual dairy cows in China.ResultsA total of 86 lactating Chinese Holstein cows were used in this study. After data editing, 704 records were obtained for calibration and validation. Six prediction models with three different machine learning algorithms and three kinds of pre-processed MIR spectra were developed for each trait. Results showed that the coefficient of determination (R2) of the best model in within-herd validation was 0.66 for NUE, 0.58 for NL and 0.63 for DMI. For external validation, reasonable prediction results were only observed for NUE, with R2 ranging from 0.58 to 0.63, while the R2 of the other two traits was below 0.50. The infrared waves from 973.54 to 988.46 cm-1 and daily milk yield were the most important variables for prediction.ConclusionThe results showed that individual NUE can be predicted with a moderate accuracy in both within-herd and external validations. The model of NUE could be used for the datasets that are similar to the calibration dataset. The prediction models for NL and 3-day moving average of DMI (DMI_a) generated lower accuracies in within-herd validation. Results also indicated that information of MIR spectra variables increased the predictive ability of models. Additionally, pre-processed MIR spectra do not result in higher accuracy than original MIR spectra in the external validation. These models will be applied to large-scale data to further investigate the genetic architecture of N efficiency and further reduce the adverse impacts on the environment after more data is collected.
| S-EPMC9830822 | biostudies-literature
Project description:Increasing pasture diversity and spatially separated sowing arrangements can potentially increase the dry matter intake of high-quality forages leading to improved animal production. This study investigated the effects of simple (two-species) and diverse (six-species) pastures planted either in mixed or spatially separated adjacent pasture strips on performance, N partitioning, and methane emission of dairy cows. Thirty-six mid-lactation Jersey cows grazed either (1) simple mixed, (2) simple spatially separated, (3) diverse mixed, or (4) diverse spatially separated pastures planted in a complete randomized block design with three replicates. Compared to simple pasture, diverse pasture had lower CP content but higher condensed tannins and total phenolic compounds with an overall positive effect on yield of milk solids, nitrogen utilization, including a reduction of N output from urine, and methane yields per dry matter eaten. The spatial separation increased legume and CP content in simple pasture but decreased NDF in both diverse and simple pastures. In conclusion, increasing diversity using pasture species with higher nutritive value and secondary compounds can help improving the production while decreasing the environmental effect of dairy farming, while spatial separation had a minor effect on feed intake and yield, possibly due to overall high-quality pastures in early spring.
| S-EPMC7460050 | biostudies-literature
Project description:Our objective was to characterize the protein coding portion of the bovine mammary gland transcriptome by magnitude of RNA-seq read counts. EBseq analysis determined no differentially expressed genes due to postruminal lysine infusion, therefore, read counts were averaged across treatment and block. DAVID functional annotation analysis was utilized to determine specific gene ontologies and KEGG pathways to describe the data by magnitude of reads.
2021-03-19 | GSE148587 | GEO
Project description:The objectives of this study were to describe the incidence of postpartum disease and to evaluate the association with serum cholesterol concentrations during the first 3 weeks after calving in grazing dairy cows. The association between non-esterified fatty acids (NEFA), β-hydroxybutyrate (BHBA), calcium and postpartum diseases was also evaluated. A total of 307 Holstein dairy cows from 6 commercial grazing herds in Osorno, Chile, were monitored from calving until 21 days in milk. Cases of retained placenta, clinical hypocalcemia and clinical mastitis were recorded by the farmer using established definitions. Twice weekly, cows were evaluated for metritis by the same veterinarian based on vaginal discharge and body temperature. Postpartum blood samples were collected weekly and analyzed for serum concentrations of cholesterol, NEFA, BHBA and calcium. Cows were considered as having subclinical ketosis if BHBA >1.2 mmol/L, and subclinical hypocalcemia if calcium <2.0 mmol/L in any of the 3 weekly samples. Overall, 56% of the cows studied developed at least one clinical or subclinical disease after calving. Incidence of individual diseases was 8.8% for retained placenta, 4.2% for clinical hypocalcemia, 11.7% for clinical mastitis, 41.1% for metritis, 19.9% for subclinical hypocalcemia and 16.6% for subclinical ketosis. Lower postpartum cholesterol in cows was associated with developing severe metritis or having more than one clinical disease after calving. For every 0.4 mmol/L decrease in serum cholesterol cows were nearly twice as likely to be diagnosed with multiple clinical diseases after calving. Higher BHBA concentrations and lower calcium concentrations during week 1 were associated with severe cases of metritis. Low serum calcium concentration during week 1 was also associated with developing more than one clinical disorder after calving. In conclusion, the incidence of postpartum diseases can be high even in grazing herds and lower serum cholesterol concentrations were associated with occurrence of clinical postpatum disorders.
| S-EPMC4373813 | biostudies-other
Project description:BackgroundFourier-transform mid-infrared (FT-MIR) spectroscopy provides a high-throughput and inexpensive method for predicting milk composition and other novel traits from milk samples. While there have been many genome-wide association studies (GWAS) conducted on FT-MIR predicted traits, there have been few GWAS for individual FT-MIR wavenumbers. Using imputed whole-genome sequence for 38,085 mixed-breed New Zealand dairy cattle, we conducted GWAS on 895 individual FT-MIR wavenumber phenotypes, and assessed the value of these direct phenotypes for identifying candidate causal genes and variants, and improving our understanding of the physico-chemical properties of milk.ResultsSeparate GWAS conducted for each of 895 individual FT-MIR wavenumber phenotypes, identified 450 1-Mbp genomic regions with significant FT-MIR wavenumber QTL, compared to 246 1-Mbp genomic regions with QTL identified for FT-MIR predicted milk composition traits. Use of mammary RNA-seq data and gene annotation information identified 38 co-localized and co-segregating expression QTL (eQTL), and 31 protein-sequence mutations for FT-MIR wavenumber phenotypes, the latter including a null mutation in the ABO gene that has a potential role in changing milk oligosaccharide profiles. For the candidate causative genes implicated in these analyses, we examined the strength of association between relevant loci and each wavenumber across the mid-infrared spectrum. This revealed shared association patterns for groups of genomically-distant loci, highlighting clusters of loci linked through their biological roles in lactation and their presumed impacts on the chemical composition of milk.ConclusionsThis study demonstrates the utility of FT-MIR wavenumber phenotypes for improving our understanding of milk composition, presenting a larger number of QTL and putative causative genes and variants than found from FT-MIR predicted composition traits. Examining patterns of significance across the mid-infrared spectrum for loci of interest further highlighted commonalities of association, which likely reflects the physico-chemical properties of milk constituents.
| S-EPMC8290608 | biostudies-literature